VecMap [1]}, [2]} creates bilingual embeddings by learning an orthogonal projection between two pre-computed monolingual vector spaces and requires only a small bilingual dictionary. We use the publicly available GoogleNews vectors for the English (available at https://code.google.com/archive/p/word2vec/), and for Spanish and Catalan we create skip-gram embeddings with 300 dimensions trained on Wikipedia data. The bilingual dictionaries are translated sentiment lexicons [3]} with 5700 pairs for English – Spanish (5271 for English – Catalan).
